1. Field of the Invention
The present invention relates to a stereo x-ray inspection apparatus and a method for forming a three-dimensional image through volume reconstruction of an image acquired from the same, and more particularly to a stereo x-ray inspection apparatus which includes one x-ray generator and two detectors to acquire two images and in which the x-ray generator and two detectors are arranged in the form of a right-angled triangle, to easily achieve mathematical development and analysis, one of the two detectors, which does not just oppose the x-ray generator, is movable and rotatable, in order to acquire images under the condition that only one detector is moved in accordance with the size of an object to be inspected, and thus to simplify control operation for the apparatus, so that it is possible to provide an apparatus capable of acquiring a more accurate image from an object moving at high speed, and a method for forming a three-dimensional image through volume reconstruction of an image acquired from the stereo x-ray inspection apparatus, which is capable of forming an image associated with not only the outline of the object, but also a hidden portion of the object.
2. Description of the Related Art
X-rays are widely used not only in the field of medicine, but also in various fields such as national defense, novel material, agricultural, environmental, and food. Recently, x-rays have been applied to x-ray image inspection, cancer treatment, analysis of isotopes, inspection of imported and exported agricultural and marine products, production of synthetic polymers, treatment of waste such as non-biodegradable wastewater, etc. In this regard, the application range of x-rays is being widened.
Mainly, x-rays are widely used in nondestructive inspection. As a representative apparatus using x-rays, there is an x-ray inspection machine installed at immigration control offices in airports.
The x-ray inspection machine is an apparatus for inspecting an object moved by a feeding device. Conventionally, such an x-ray inspection machine includes a single x-ray generator and a detector. Due to such a configuration, it is difficult for the x-ray inspection machine to accurately check the shape of an object because only a two-dimensional image is acquired by the machine. Of course, a three-dimensional nondestructive image such as a computed tomography (CT) image or a magnetic resonance image (MRI) may be acquired. Although such an image is usable for medical purposes or the like, which do not require high-speed inspection, it cannot be used in physical distribution systems requiring high-speed inspection for continuously-moving large-scale objects because there is a limitation on inspection speed. To this end, research should be conducted into apparatuses capable of solving the above-mentioned problem.
Meanwhile, a three-dimensional image is acquired by combining images acquired by two cameras having different view points, namely, stereo images.
The positions of images respectively acquired by projecting left and right images of an object to be inspected are different from each other. This difference is referred to as “disparity”. Generally, the disparity is determined taking into consideration only an x-direction difference. This is because there is only a horizontal disparity in that, typically, stereo cameras are installed to be horizontally arranged.
In order to derive a disparity image, disparities of all pixels with respect to one of left and right stereo images are derived, and an image is formed based on the derived disparities. In order to acquire such a disparity image, it is necessary to derive disparities of the associated image at all positions. The method for acquiring a disparity image is referred to as “stereo matching”.
The most general stereo matching technology is a method for comparing particular regions of two images with each other to find identity of the images. This method is referred to as “template matching”. Using such a template matching method, disparity of stereo images is estimated. Based on the estimated disparity, the stereo images are estimated.
Template matching is carried out as follows.
The following description will be given in conjunction with, for example, a red square region in a left image shown in FIG. 1. It is assumed that the template in the left image of FIG. 1 is referred to as “WL”, and the template in the right image of FIG. 1 is referred to as “WR”, and both the positions of the two templates WL and WR are located on a y axis. This means that the left and light cameras of a stereo camera unit are located to be movable only in a horizontal direction. Let's assume that the coordinates of a central pixel of the template WL are “xL, yL”, and the coordinates of a central pixel of the template WR are “xL−d, yL”. In this case, “d” represents the disparity between the left and right images. When the disparity d is not zero, this means that an object to be inspected is located at different positions on a horizontal axis in the two images. This is referred to as a “disparity of stereo images”.
In order to derive a disparity of stereo images, the coordinates of the template region Wm(x, y) are derived while varying the disparity d along a horizontal line within a possible range for all pixels of the right image, as follows:
            W      m        ⁡          (              x        ,        y            )        =      {          u      ,              v        |                              x            -                          m              2                                ≤          u          ≤                      x            +                          m              2                                          ,                        Y          -                      m            2                          ≤        v        ≤                  y          +                      m            2                                }  
The template regions Wm(x, y) in the left and right images are compared with each other, as expressed by the following expression. In the following expression, “IL” and “IR” represent brightness values of the left and right images, respectively. Referring to the following expression, the difference between the brightness values of two pixels is squared, and this squaring operation is repeated for all pixels. The results of the squaring operations repeated for all pixels are then summed. The resultant value is referred to as a “cost value Cr(x, y, d)” of the templates WL and WR. The procedure for finding the disparity d, which corresponds to a minimum value of the derived cost values Cr(x, y, d), is a procedure for estimating the disparity of stereo images.
            C      r        ⁡          (              x        ,        y        ,        d            )        =            ∑                        (                      u            ,            v                    )                ∈                              W            m                    ⁡                      (                          x              ,              y                        )                                ⁢                  [                                            I              L                        ⁡                          (                              u                ,                v                            )                                -                                    I              R                        ⁡                          (                                                u                  -                  d                                ,                v                            )                                      ]            2      
However, the above procedure is used to acquire stereo images through a general camera, as shown in FIG. 2(a). This procedure cannot be used to acquire an x-ray image as shown in FIG. 2(b).
Furthermore, even when an x-ray image is acquired, three-dimensional reconstruction of an outline is possible only for an outline aligned with the orientation of the object and the line extending between an x-ray source and a sensor. Furthermore, only the outline of an outer surface of the object is mainly reconstructed, and the outline of an inner surface of the object is hardly indicated in an acquired image. Thus, it is insufficient to reconstruct the entire outline.